Portable/Wearable　(P/W)　electroencephalography　(EEG)　devices　capture　and　analyze　EEG　signals,　which　are　widely　used　in　different　research　fields,　such　as　consumer　psychology　prediction,　attention　and　fatigue　monitoring.　Nonetheless,　EEG　signals　obtained　through　P/W　devices　are　sensitive　to　environmental　conditions　and　physiological　activities,　rendering　real-time　denoising　a　challenge　on　computation　and　memory　limited　consumer　electronics　(CE).　In　this　work,　we　propose　a　lightweight　network　of　P/W　devices　for　real-time　EEG　signal　denoising　(LTDNet-EEG).　Specifically,　LTDNet-EEG　performs　automatic　linearized　modeling　of　nonlinear　EEG　signals　via　Taylor　series　expansion,　then　utilizes　a　Kalman　smoothing　filter　to　remove　noise　from　EEG　signals　and　designs　a　lightweight　network　based　on　depthwise　separable　convolution　(DSC)　to　update　Kalman　gain　and　other　parameters.　Besides,　it　applies　data　layout　and　common　subexpression　elimination　to　optimize　model　structure　and　code　computation　respectively.　Experiments　on　the　benchmark　EEGdenoiseNet　database　show　that　LTDNet-EEG　outperforms　the　existing　state-of-the-art　algorithm.　Additionally,　the　LTDNet-EEG　can　be　effectively　implemented　on　the　hardware　platform　equipped　with　a　4th　generation　Raspberry　Pi　(4GB　RAM,　16GB　Flash).　Compared　to　training　and　reasoning　on　CPU,　the　LTDNet-EEG　with　optimized　approaches　achieves　approximately　a　2.5-fold　reduction　in　execution　time　which　has　great　potential　widely　to　be　used　in　CE.　IEEE